README.md in convolver-0.1.2 vs README.md in convolver-0.2.0
- old
+ new
@@ -1,21 +1,17 @@
# Convolver
[](http://travis-ci.org/neilslater/convolver)
-Adds a convolve operation to NArray floats. It is around 250 times faster than equivalents
-in pure Ruby.
+Calculates discrete convolution between two multi-dimensional arrays.
+See http://en.wikipedia.org/wiki/Convolution
-The gem makes convolution via FFTW3 library available. This is faster for convolutions with
-larger kernels and signals. The relationship is complex, but as a rule of thumb, the kernel
-needs to be around 1000 entries or larger before it is worth switching to FFTW3-based convolves.
-
## Planned features
-The *convolver* gem will eventually contain a basic kit for creating, training and running convolutional
-neural networks. As a side effect of this plan, it will also contain efficient code for
-calculating signal convolutions for other types of analysis.
+The *convolver* gem will eventually contain a basic kit for creating, training and running
+convolutional neural networks. As a side effect of this plan, it currently contains code for
+calculating floating-point convolutions for other types of analysis.
## Installation
### Dependency: FFTW3
@@ -51,15 +47,13 @@
kernel in the same dimension.
* Convolver expects input a and kernel b to have the same rank, and for the kernel to be same size
or smaller in all dimensions as the input.
-FFTW3 convolution:
-
- a = NArray[0.3,0.4,0.5]
- b = NArray[1.3, -0.5]
- c = Convolver.convolve_fftw3( a, b )
- => NArray.float(2): [ 0.19, 0.27 ]
+ * Convolver.convolve will try to choose the faster of two approaches it has coded. In general,
+small convolutions are processed directly by multiplying out all combinations and summing them,
+and large convolutions are processed using FFTW3 to convert to frequency space where convolution
+is simpler and faster to calculate, then convert back.
## Contributing
1. Fork it
2. Create your feature branch (`git checkout -b my-new-feature`)